Next Article in Journal
Assessment of 137Cs and 40K Transfer Factors in Croatian Agricultural Systems and Implications for Food Safety
Previous Article in Journal
Long-Term Performance of Passive Volatile Organic Compounds (VOCs) Samplers for Indoor Air
Previous Article in Special Issue
Machine Learning for Flood Resiliency—Current Status and Unexplored Directions
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Sustainable Water Resource Management: Challenges and Opportunities

1
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
2
School of Management, Chengdu University of Information Technology, Chengdu, 610225, China
3
SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Beijing 100049, China
4
CAS Center for Excellence in Quaternary Science and Global Change, Xi’an 710061, China
5
Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
*
Author to whom correspondence should be addressed.
Environments 2025, 12(8), 268; https://doi.org/10.3390/environments12080268 (registering DOI)
Submission received: 17 July 2025 / Revised: 21 July 2025 / Accepted: 24 July 2025 / Published: 1 August 2025
(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)

1. Introduction

Water is a basic human necessity, and the amount of water on Earth remains fairly constant [1,2]. As global population growth and economic development continues to expand, water demand continues to rise [3,4]. This creates mounting pressure between water supply and growing demand [5], making sustainable water resource management increasingly critical. Hydrological models serve as the primary tool for sustainable water resource management, providing a practical way to explore ‘what if’ scenarios where direct experimentation on watersheds or river systems is not feasible [6,7]. Hydrological models are typically classified into three categories based on their mathematical approach: empirical, conceptual, and physically based models [8]. In recent decades, the rapid development of artificial intelligence has led to the emergence of a fourth category of hydrological models known as machine learning models [9,10]. Unlike traditional empirical approaches, these machine learning models can learn complex nonlinear relationships from high-dimensional datasets without relying on predefined mathematical equations [11]. They have been widely applied to rainfall–runoff modeling [12,13], evapotranspiration modeling [14,15], groundwater–surface water interactions [16,17], and many other hydrological processes. This development reflects the field’s evolution toward more interdisciplinary approaches.
As hydrological modeling and water resource management continue to advance, they face emerging and increasingly complex challenges that require innovative, interdisciplinary solutions. This Special Issue, “Hydrological Modeling and Sustainable Water Resources Management”, explores several of these pressing challenges, organizing them into four major categories: (1) prediction and forecasting challenges, (2) solutions to data gaps, (3) water system performance and optimization, and (4) integrated impact assessment. Prediction and forecasting challenges primarily involve enhancing model performance under a wide range of conditions. One of the most difficult tasks is predicting extreme events such as floods and droughts, due to the inherently complex and highly nonlinear behavior of hydrological systems [18]. Additional uncertainties arise from model structure, parameter selection, and initial conditions, which further complicate accurate prediction and forecasting. Data gaps refer to limitations in data availability that hinder model development and highlight the need to address missing, rare, or outdated information relevant to a given system. Furthermore, water systems face specific environmental challenges, where improving the design and operation of these systems to maximize effectiveness while minimizing environmental and economic costs remains a significant concern. Lastly, water systems are influenced by a wide array of factors, with climate change being one of the most significant drivers. Understanding how various stressors interact simultaneously and developing frameworks to analyze and manage these complex interactions continues to be a major challenge. This Special Issue includes 14 original research articles and two reviews and explores solutions related to the four key challenges in achieving sustainable water resource management.

2. An Overview of Published Articles

The following brief overview serves only as a guide to these published articles; we encourage readers to explore the full texts according to their interests.

2.1. Prediction and Forecasting Challenges

The first article by Piraei et al. (contribution 1) develops machine learning approaches to predict drought conditions using the standardized precipitation index (SPI), exploring the accurate drought prediction across different climate zones with varied lag times.
The contribution by Larance et al. (contribution 2) develops a SARIMA model to forecast dissolved oxygen and water temperature in the Athabasca River, achieving strong performance (R2 = 0.67–0.97) and providing a practical tool for predicting low dissolved oxygen events that threaten aquatic ecosystems.
The third article by Lee et al. (contribution 3) integrates SWAT with machine learning to predict specified alpha factors, improving baseflow simulation accuracy and providing a web-based tool for practical watershed management applications.

2.2. Data Gaps

The research by Villanueva et al. (contribution 4) develops a cost-effective method to create digital elevation models using aerial photogrammetry with acceptable accuracy trade-offs, providing an accessible alternative to expensive surveys in data-scarce regions.
In the fifth article, Yan et al. (contribution 5) apply the Budyko framework for actual evapotranspiration estimation in Ontario using remote sensing and single-parameter calibration, achieving good performance (NSE = 0.74) and providing an alternative to data-intensive traditional methods.
The article by Wang et al. (contribution 6) develops methods to monitor small water bodies using aerial images, solving the problem of undocumented or outdated information on small freshwater resources.
Pascual et al. (contribution 7) examine cultural and socio-economic relationships on spring ecohydrogeology in Mediterranean climate zones through a global literature review and case studies, addressing the lack of comprehensive understanding of cultural–ecological interactions.

2.3. Water System Performance and Optimization

The article by Coronado-Hernández et al. (contribution 8) develops a simplified method for computing extreme hydrographs in dam design using frequency analysis data. The approach achieved 11.9% validation error at a real hydropower plant, providing engineers with a more efficient tool for designing critical dam infrastructure.
The research from Assar et al. (contribution 9) compares computational methods for predicting backwater depth at bridge constrictions, finding that energy and momentum methods provide the most accurate results while WSPRO overestimates and Yarnell’s method underestimates afflux values.
Umukiza et al. (contribution 10) develop GIS-based methods to identify optimal dam locations, addressing the failure of existing poorly sited dams that dry out during drought periods.
The article by Doro (contribution 11) develops algorithms for simulating drainage water recycling systems using the APEX model. The model accurately predicted sediment loss reduction.
Razack et al. (contribution 12) assess Djibouti’s Dalha aquifer using numerical modeling and find current extraction is already unsustainable, with declining water tables projected under climate change scenarios, making any increased exploitation unfeasible despite growing regional demands.

2.4. Integrated Impact Assessment

The research by Hibbs (contribution 13) models groundwater flow in the transboundary Southeastern Hueco Aquifer, finding current recharge (3.5 mm/year) is insufficient compared to historical levels (10.4 mm/year).
The work of Grosser et al. (contribution 14) models climate change impacts on Germany’s Gersprenz catchment through 2100, projecting severe discharge reductions (85% summer, 38% autumn) and excessive water temperatures, while providing a framework for spatially targeted adaptation strategies.
The contribution by Kimbi et al. (contribution 15) uses SWAT modeling to assess 30-year urbanization impacts on a Japanese catchment, finding land use change caused a 34.9% groundwater recharge reduction and recommending sustainable management practices to balance urban development with water sustainability.
The final contribution by Gervasio et al. (contribution 16) examines climate change effects on Po River denitrification through sediment core experiments, finding that increased temperatures and nitrate enhance nitrogen removal processes, potentially reducing coastal eutrophication risks during summer.

Acknowledgments

The Guest Editors would like to thank all the authors for their contributions. We also extend our special thanks to the reviewers for their dedicated work, which has greatly improved the quality of this Special Issue.

Conflicts of Interest

The authors declare no conflict of interest.

List of Contributions

  • Piraei, R.; Niazkar, M.; Gangi, F.; Eryılmaz Türkkan, G.; Afzali, S.H. Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models. Hydrology 2024, 11, 163. https://doi.org/10.3390/hydrology11100163.
  • Larance, S.; Wang, J.; Delavar, M.A.; Fahs, M. Assessing Water Temperature and Dissolved Oxygen and Their Potential Effects on Aquatic Ecosystem Using a SARIMA Model. Environments 2025, 12, 25. https://doi.org/10.3390/environments12010025.
  • Lee, J.; Han, J.; Engel, B.; Lim, K.J. Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning. Environments 2025, 12, 94. https://doi.org/10.3390/environments12030094.
  • Villanueva, J.R.E.; Pérez-Montiel, J.I.; Nardini, A.G.C. DEM Generation Incorporating River Channels in Data-Scarce Contexts: The “Fluvial Domain Method”. Hydrology 2025, 12, 33. https://doi.org/10.3390/hydrology12020033.
  • Yan, Z.; Li, Z.; Baetz, B. Evapotranspiration Estimation with the Budyko Framework for Canadian Watersheds. Hydrology 2024, 11, 191. https://doi.org/10.3390/hydrology11110191.
  • Wang, C.; Pellett, C.A.; Tan, H.; Arrington, T. Aerial Imagery and Surface Water and Ocean Topography for High-Resolution Mapping for Water Availability Assessments of Small Waterbodies on the Coast. Environments 2025, 12, 168. https://doi.org/10.3390/environments12050168.
  • Pascual, R.; Piana, L.; Bhat, S.U.; Castro, P.F.; Corbera, J.; Cummings, D.; Delgado, C.; Eades, E.; Fensham, R.J.; Fernández-Martínez, M.; et al. The Cultural Ecohydrogeology of Mediterranean-Climate Springs: A Global Review with Case Studies. Environments 2024, 11, 110. https://doi.org/10.3390/environments11060110.
  • Coronado-Hernández, O.E.; Fuertes-Miquel, V.S.; Arrieta-Pastrana, A. The Development of a Hydrological Method for Computing Extreme Hydrographs in Engineering Dam Projects. Hydrology 2024, 11, 194. https://doi.org/10.3390/hydrology11110194.
  • Haji Amou Assar, K.; Atabay, S.; Yilmaz, A.G.; Sharifi, S. Backwater Level Computations Due to Bridge Constrictions: An Assessment of Methods. Hydrology 2024, 11, 220. https://doi.org/10.3390/hydrology11120220.
  • Umukiza, E.; Abagale, F.K.; Apusiga Adongo, T.; Petroselli, A. Suitability Assessment and Optimization of Small Dams and Reservoirs in Northern Ghana. Hydrology 2024, 11, 166. https://doi.org/10.3390/hydrology11100166.
  • Doro, L.; Wang, X.; Jeong, J. Simulating Agricultural Water Recycling Using the APEX Model. Environments 2024, 11, 244. https://doi.org/10.3390/environments11110244.
  • Razack, M.; Jalludin, M.; Birhanu, B. Water Resource Assessment and Management in Dalha Basalts Aquifer (SW Djibouti) Using Numerical Modeling. Hydrology 2025, 12, 73. https://doi.org/10.3390/hydrology12040073.
  • Hibbs, B. Analyzing Aquifer Flow Capacity and Fossil Hydraulic Gradients Through Numerical Modeling: Implications for Climate Change and Waste Disposal in Arid Basins. Environments 2025, 12, 79. https://doi.org/10.3390/environments12030079.
  • Grosser, P.F.; Schmalz, B. Assessing the Impacts of Climate Change on Hydrological Processes in a German Low Mountain Range Basin: Modelling Future Water Availability, Low Flows and Water Temperatures Using SWAT+. Environments 2025, 12, 151. https://doi.org/10.3390/environments12050151.
  • Kimbi, S.B.; Onodera, S.-i.; Wang, K.; Kaihotsu, I.; Shimizu, Y. Assessing the Impact of Urbanization and Climate Change on Hydrological Processes in a Suburban Catchment. Environments 2024, 11, 225. https://doi.org/10.3390/environments11100225.
  • Gervasio, M.P.; Castaldelli, G.; Soana, E. The Response of Denitrification to Increasing Water Temperature and Nitrate Availability: The Case of a Large Lowland River (Po River, Northern Italy) under a Climate Change Scenario. Environments 2024, 11, 179. https://doi.org/10.3390/environments11080179.

References

  1. Trenberth, K.E.; Smith, L.; Qian, T.; Dai, A.; Fasullo, J. Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data. J. Hydrometeorol. 2007, 8, 758–769. [Google Scholar] [CrossRef]
  2. Gleick, P.H. Water in Crisis: Paths to Sustainable Water Use. Ecol. Appl. 1998, 8, 571–579. [Google Scholar] [CrossRef]
  3. Boretti, A.; Rosa, L. Reassessing the Projections of the World Water Development Report. npj Clean Water 2019, 2, 15. [Google Scholar] [CrossRef]
  4. Vörösmarty, C.J.; Green, P.; Salisbury, J.; Lammers, R.B. Global Water Resources: Vulnerability from Climate Change and Population Growth. Science 2000, 289, 284–288. [Google Scholar] [CrossRef]
  5. Hoekstra, A.; Huynen, M. Balancing the World Water Demand and Supply. In Transitions in a Globalising World; Routledge: Abingdon, UK, 2002; ISBN 978-0-203-73519-0. [Google Scholar]
  6. Nath, N.K.; Das, P.; Mishra, L.R.; Kumar, A.; Suryawanshi, S.L.; Gautam, V.K. Hydrological Modeling and Simulation for Water Resource Assessment. In Integrated Management of Water Resources in India: A Computational Approach: Optimizing for Sustainability and Planning; Yadav, A.K., Yadav, K., Singh, V.P., Eds.; Springer Nature: Cham, Switzerland, 2024; pp. 43–58. ISBN 978-3-031-62079-9. [Google Scholar]
  7. Singh, V.P. Hydrologic Modeling: Progress and Future Directions. Geosci. Lett. 2018, 5, 15. [Google Scholar] [CrossRef]
  8. Devia, G.K.; Ganasri, B.P.; Dwarakish, G.S. A Review on Hydrological Models. Aquat. Procedia 2015, 4, 1001–1007. [Google Scholar] [CrossRef]
  9. Nourani, V.; Hosseini Baghanam, A.; Adamowski, J.; Kisi, O. Applications of Hybrid Wavelet–Artificial Intelligence Models in Hydrology: A Review. J. Hydrol. 2014, 514, 358–377. [Google Scholar] [CrossRef]
  10. Shen, C. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resour. Res. 2018, 54, 8558–8593. [Google Scholar] [CrossRef]
  11. Nearing, G.S.; Kratzert, F.; Sampson, A.K.; Pelissier, C.S.; Klotz, D.; Frame, J.M.; Prieto, C.; Gupta, H.V. What Role Does Hydrological Science Play in the Age of Machine Learning? Water Resour. Res. 2021, 57, e2020WR028091. [Google Scholar] [CrossRef]
  12. Zhang, B.; Ouyang, C.; Cui, P.; Xu, Q.; Wang, D.; Zhang, F.; Li, Z.; Fan, L.; Lovati, M.; Liu, Y.; et al. Deep Learning for Cross-Region Streamflow and Flood Forecasting at a Global Scale. Innovation 2024, 5, 100617. [Google Scholar] [CrossRef] [PubMed]
  13. Zhou, P.; Li, C.; Li, Z.; Cai, Y. Assessing Uncertainty Propagation in Hybrid Models for Daily Streamflow Simulation Based on Arbitrary Polynomial Chaos Expansion. Adv. Water Resour. 2022, 160, 104110. [Google Scholar] [CrossRef]
  14. Agrawal, Y.; Kumar, M.; Ananthakrishnan, S.; Kumarapuram, G. Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm. Water Resour. Manag. 2022, 36, 1025–1042. [Google Scholar] [CrossRef]
  15. Granata, F. Evapotranspiration Evaluation Models Based on Machine Learning Algorithms—A Comparative Study. Agric. Water Manag. 2019, 217, 303–315. [Google Scholar] [CrossRef]
  16. Kim, S.; Lee, E.; Hwang, H.-T.; Pyo, J.; Yun, D.; Baek, S.-S.; Cho, K.H. Spatiotemporal Estimation of Groundwater and Surface Water Conditions by Integrating Deep Learning and Physics-Based Watershed Models. Water Res. X 2024, 23, 100228. [Google Scholar] [CrossRef] [PubMed]
  17. Tran, H.; Leonarduzzi, E.; De la Fuente, L.; Hull, R.B.; Bansal, V.; Chennault, C.; Gentine, P.; Melchior, P.; Condon, L.E.; Maxwell, R.M. Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML. Water 2021, 13, 3393. [Google Scholar] [CrossRef]
  18. Brunner, M.I.; Slater, L.; Tallaksen, L.M.; Clark, M. Challenges in Modeling and Predicting Floods and Droughts: A Review. WIREs Water 2021, 8, e1520. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhou, P.; Zhang, Q.; Zhang, F.; Li, Z. Sustainable Water Resource Management: Challenges and Opportunities. Environments 2025, 12, 268. https://doi.org/10.3390/environments12080268

AMA Style

Zhou P, Zhang Q, Zhang F, Li Z. Sustainable Water Resource Management: Challenges and Opportunities. Environments. 2025; 12(8):268. https://doi.org/10.3390/environments12080268

Chicago/Turabian Style

Zhou, Pengxiao, Qianqian Zhang, Fei Zhang, and Zoe Li. 2025. "Sustainable Water Resource Management: Challenges and Opportunities" Environments 12, no. 8: 268. https://doi.org/10.3390/environments12080268

APA Style

Zhou, P., Zhang, Q., Zhang, F., & Li, Z. (2025). Sustainable Water Resource Management: Challenges and Opportunities. Environments, 12(8), 268. https://doi.org/10.3390/environments12080268

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop